University of Tsukuba

About: University of Tsukuba is a education organization based out in Tsukuba, Ibaraki, Japan. It is known for research contribution in the topics: Population & Gene. The organization has 36352 authors who have published 79483 publications receiving 1934752 citations. The organization is also known as: Tsukuba daigaku & Tsukuba University.

Electronic structure of early 3d-transition-metal oxides by analysis of the 2p core-level photoemission spectra.

Abstract: The electronic structures of a wide range of early transition-metal (TM) compounds, including Ti and V oxides with metal valences ranging from 2+ to 5+ and formal d-electron numbers ranging from 0 to 2, have been investigated by a configuration-interaction cluster model analysis of the core-level metal 2p x-ray photoemission spectra (XPS). Inelastic energy-loss backgrounds calculated from experimentally measured electron-energy-loss spectra (EELS) were subtracted from the XPS spectra to remove extrinsic loss features. Parameter values deduced for the charge-transfer energy Delta and the d-d Coulomb repulsion energy U are shown to continue the systematic trends established previously for the late TM compounds, giving support to a charge-transfer mechanism for the satellite structures. The early TM compounds are characterized by a large metal d-ligand p hybridization energy, resulting in strong covalency in these compounds. Values for Delta and U suggest that many early TM compounds should be reclassified as intermediate between the charge-transfer regime and the Mott-Hubbard regime.

Performance comparison of FPGA, GPU and CPU in image processing

Abstract: Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve high performance. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. However, the cores are grouped, and data transfer between the groups is very limited. Programming tools for FPGA, SIMD instructions on CPU and a large number of cores on GPU have been developed, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of FPGA, GPU and CPU using three applications in image processing; two-dimensional filters, stereo-vision and k-means clustering, and make it clear which platform is faster under which conditions.